What are Data Applications? Definition, Examples & Use Cases

Organizations depend on data to inform business intelligence, analytics, edge computing, and more. From small data sets to vast oceans of data, an organization's data can include structured data, like financial transactions, and unstructured data, like social media posts and video. It can come from a wide range of sources, such as weather satellites and traffic cameras. There is data everywhere you look, and it can be overwhelming to figure out how to best harness data to grow your business. The rise of data applications—especially those that are capable of handling big data—has fundamentally transformed all aspects of how businesses interact with information. This article will discuss what data applications are, what they consist of, the challenges of building and implementing them, and the benefits across industries.
What are data applications?
Most of us are all familiar with what software applications can do—they perform tasks such as creating written documents, spreadsheets, and presentations, among many other things. While a software application performs specific tasks, a data application focuses on handling data in specific ways. A data application is software that collects, processes, analyzes, stores, and visualizes data to generate actionable insights. In some cases, data applications also provide automated tasks related to data management.
Components of data applications
There is so much raw data, but how do you harness the data to enhance your business? Data applications have several components to help with that task. First, the data must be ingested quality data sources. Then, the data needs to be processed into a consistent format. Then, there's reliable data storage and management. Finally, you need to be able to visualize the data in an intuitive way so you can analyze it to gain useful insights. We go into this a bit more below:
Data sources and ingestion: Data applications take in data from a variety of sources, including SQL and NoSQL databases, data warehouses, APIs from various platforms such as social media and finance, sensor data, and cloud data, among other sources. Some data is proprietary or personal, which needs to be handled securely, while other kinds of data, like data about retail products, is available to the public. For more information about how data is ingested, see What is Data Ingestion? Types, Tools & Differences with ETL.
Data processing and transformation: Since data can come from multiple sources, you need to normalize the data to bring it to a standard format, such as correcting different date and currency formats. In addition, the data can contain duplicates or missing information. Data applications streamline the process of normalizing data into a unified form.
Data storage and management: A key component of a data application is secure data storage and management. You want to control who has access to the data, and you want to ensure its safety in the event of a temporary outage or even a natural disaster.
Analysis engine: The analysis engine is the component of the data application responsible for processing and analyzing data to extract insights for decision-making. It is the component that gives data meaning. Analysis can include statistical models, natural language processing, and artificial intelligence (AI).
Visualization layer: The visualization layer makes it easier for users to understand patterns and trends in the data. Types of visualizations include charts such as pie and line charts, heatmaps, and network diagrams. The visualization layer aids in data exploration. For more information about data exploration, see What Is Data Exploration? Definition & Importance.
User interface: The user interface component of a data application is where users interact with the application. You want the interface to be as intuitive as possible, so that different types of users can obtain insights. Key user interface features include dashboards, data visualization, search tools, and report generation.
6 benefits of data applications
Here are six reasons why data applications are beneficial to your business.
Highly customizable: The users who understand the data best do not necessarily have a programming background. They want to customize their application so they can see the data that's most important to them. Many data applications, like Tableau and Power BI, have customizable drag-and-drop widgets, such as tables and graphs, that users can add to dashboards. That way, they can focus only on the data they need.
Improved efficiency: Data applications improve efficiency by streamlining data ingestion and transformation from various sources. Many data applications also incorporate automation. You can set up triggers when a certain threshold is reached, or at certain time intervals. The advantage here is that all stakeholders can have access to the same information, making collaboration easier.
Better informed decision-making: Data applications streamline the process of decision making. Even experts in the field cannot rely on hunches—they need access to real-time data to back up their decisions. Faster decision making allows businesses to react more quickly to changes or capitalize on new opportunities. For more information about the importance of real-time data, see Real-Time Data: What Is It, Why It Matters & How It Works.
Improved customer experience: Data applications allow businesses to provide personalized content and recommendations to their customers. They can also use data from past purchases and use machine learning to predict the customer’s future needs.
Increased flexibility: Real-time data allows business to adapt quickly to changing needs. As the business grows, data applications can adapt and scale along with it. For example, data applications can pull data from different systems, including legacy systems, without a complete system overhaul.
More competitive advantages: Business that can harness their data for better decision making have a competitive advantage. When data analytics detects a market shift, business can respond quickly. Businesses can collect data on competitors, finding ways to differentiate themselves in the market.
Challenges of building and implementing data applications
Implementing a data application is valuable, but it is not without challenges. The following are some of the challenges you will come across.
Low latency concerns: “Latency” refers to the delay between a user’s action and the application’s response. Certain applications, such as financial trading, require response times in milliseconds. Data applications employ various strategies to keep latency as low as possible, such as load balancing and data caching.
Non-compatible legacy systems: Legacy systems can pose challenges in building data applications. Legacy data may not conform to current standards, such as JSON or XML. The system may have been created without application programming interface (API) support, and therefore requires custom connectors to access the data.
Large volumes of data: Large volumes of data can pose delays with the ingestion and transformation components of the data application. You also need to contend with the challenge of storing all the ingested data. Also, do you have enough compute power to analyze all the data?
Integration capabilities: The data application must be able to integrate the data from different sources, including legacy systems. While you may have disparate source data, you need to create a seamless flow of information across different systems. For more information about application and data integration tools, see App Integration vs. Data Integration: How to Choose the Right Tools.
Data compatibility: Different systems store data in different formats and can include both structured and unstructured data. Your data can exist in SQL-based tables or can be non-structured documents and time-series data. The data application must be able to handle all kinds of data to improve efficiency and provide business insights.
High concurrency, availability, and scalability: Concurrency means the data application can handle many users or processes at the same time without a degradation in performance. An e-commerce platform must be able to handle simultaneous users placing orders and browsing for products. Availability is important because any down time can affect your bottom line. Data applications handle availability by using replication and load balancing. Scalability refers to the ability of a data application to handle increasing volumes of data, users, or workload, without sacrificing performance. Horizontal scaling and distributed systems help with scalability.
Data security risks: Handling large quantities of data is not without risk. Having adequate security is crucial for maintaining data integrity and customer privacy. Some ways to mitigate risk are through multi-factor authentication (MFA), role-based access control (RBAC), and data encryption. Other information is subject to industry-wide compliance regulations, such as HIPAA with healthcare data.
Business data applications, examples and use cases
All types of industries can benefit from data applications. The following is a sample of what data applications can offer to a variety of business sectors.
Retail: Data applications can enhance the retail customer’s experience by harnessing data from past purchases. This enables retailers to provide a personalized shopping experience by making recommendations and offering targeted sales.
Healthcare: Healthcare data applications manage electronic health records (EHR), which streamlines patient care. They can also provide predictive analytics about the best course of treatment based on analysis of vast amounts of medical literature and patient data.
Finance: In finance, data applications can provide fraud detection. Since these applications can analyze so much data, they can determine the transactions that are out of the ordinary. Data applications can also perform market risk analysis, allowing financial corporations to assess and hedge risks. Data applications are also the brains behind quantitative hedge funds that use algorithms to analyze market data for patterns.
Manufacturing: In manufacturing, data applications provide help with logistics and predictive maintenance. These applications can optimize supply chain management by providing real-time inventory levels and forecasting demand. Data applications can also obtain real-time data from equipment sensors to detect defects or unusual behavior.
CData Virtuality can help
CData Virtuality provides an enterprise semantic layer that enables you to navigate and query large volumes of data. With instant access to over 300 data sources, CData Virtuality empowers faster insights. CData Virtuality's centralized controls ensure data quality, security, and compliance, making it a premier data application.
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